Ingest Nested JSON to Delta | Dataford Interview Questions

Your question is Ingest Nested JSON to Delta. Start with the requirements on the right.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem

Write a clean, modular Python function to parse and ingest highly nested JSON logs into a partitioned Delta table. Implement the transformation layer without requiring Spark APIs: parse each JSON document, flatten nested objects using dotted keys, preserve arrays as values, and group records by a nested partition field. The function receives JSON strings and returns a dictionary mapping partition values to normalized records ready for Delta ingestion.

Signature: def parse_and_partition_logs(logs, partition_field):

Input/output: logs is a list of JSON strings; partition_field is a dotted path; return {partition_value: [record, ...]}. Raise ValueError for invalid JSON, missing partition fields, or non-scalar partition values.

Constraints

0 <= len(logs) <= 10^4
Each log is a valid JSON string representing an object unless testing error handling
Nested object depth is at most 100
partition_field contains one or more non-empty dot-separated keys
Partition values must be scalar JSON values
Array values are preserved as leaf values

Function Signature

def parse_and_partition_logs(logs, partition_field):

Your question is Ingest Nested JSON to Delta. Start with the requirements on the right.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Signature: def parse_and_partition_logs(logs, partition_field):

Constraints

0 <= len(logs) <= 10^4
Each log is a valid JSON string representing an object unless testing error handling
Nested object depth is at most 100
partition_field contains one or more non-empty dot-separated keys
Partition values must be scalar JSON values
Array values are preserved as leaf values

Function Signature

def parse_and_partition_logs(logs, partition_field):